Quick Demo

Sachin Yadav, IIT Gandhinagar, sachin.yadav@iitgn.ac.in

IRIS Flower data set

The data set contains information about three species of IRIS flowers namely:

• Iris setosa
• Iris versicolor
• Iris virginica

Four features are collected from each sample, sepal-length, sepal-width, petal-length and petal-width in centi-meters.

# Common imports
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

import plotly.express as px

iris_df = pd.read_csv("./data/IRIS.csv.gz", compression="gzip")
iris_df.sample(5)

	Unnamed: 0	sepal_length	sepal_width	petal_length	petal_width	species
144	144	6.7	3.3	5.7	2.5	Iris-virginica
48	48	5.3	3.7	1.5	0.2	Iris-setosa
52	52	6.9	3.1	4.9	1.5	Iris-versicolor
23	23	5.1	3.3	1.7	0.5	Iris-setosa
78	78	6.0	2.9	4.5	1.5	Iris-versicolor

fig = px.scatter_3d(iris_df, x='sepal_length', y='sepal_width', z='petal_width',
              color='species', template="plotly_dark")
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

COVID-19 Dataset

covid_df = pd.read_csv("./data/owid-covid-data.csv.gz", compression="gzip")
covid_df.sample(5)

	Unnamed: 0	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	...	female_smokers	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million
154914	154914	UKR	Europe	Ukraine	2020-08-19	98658.0	2005.0	1736.286	2182.0	30.0	...	13.5	47.4	NaN	8.800	72.06	0.779	NaN	NaN	NaN	NaN
139575	139575	KOR	Asia	South Korea	2020-11-06	27284.0	89.0	110.429	477.0	1.0	...	6.2	40.9	NaN	12.270	83.03	0.916	NaN	NaN	NaN	NaN
101362	101362	MNE	Europe	Montenegro	2020-07-02	616.0	40.0	28.857	12.0	0.0	...	44.0	47.9	NaN	3.861	76.88	0.829	NaN	NaN	NaN	NaN
95205	95205	MLT	Europe	Malta	2020-03-05	NaN	NaN	NaN	NaN	NaN	...	20.9	30.2	NaN	4.485	82.53	0.895	NaN	NaN	NaN	NaN
133226	133226	SYC	Africa	Seychelles	2021-12-24	24249.0	0.0	28.857	131.0	0.0	...	7.1	35.7	NaN	3.600	73.40	0.796	NaN	NaN	NaN	NaN

5 rows × 68 columns

required_columns = ["iso_code", "location", "continent", "date", "new_cases_smoothed", "total_cases"]
covid_df = covid_df.dropna(subset = required_columns)

covid_df = covid_df.sort_values("date")

covid_df[['iso_code', 'location']].sample(8)

	iso_code	location
130786	SAU	Saudi Arabia
113504	NOR	Norway
81105	OWID_KOS	Kosovo
82978	LAO	Laos
2648	DZA	Algeria
72942	IRQ	Iraq
106334	NPL	Nepal
76316	JAM	Jamaica

Covid-19 New Cases Worldwide on 23 December 2021

covid_day_df = covid_df[covid_df.date == "2021-12-23"]
covid_day_df.sample(5)

	Unnamed: 0	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	...	female_smokers	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million
76209	76209	ITA	Europe	Italy	2021-12-23	5517054.0	44585.0	29839.143	136245.0	168.0	...	19.8	27.8	NaN	3.18	83.51	0.892	NaN	NaN	NaN	NaN
136996	136996	SLB	Oceania	Solomon Islands	2021-12-23	20.0	0.0	0.000	NaN	NaN	...	NaN	NaN	35.890	1.40	73.00	0.567	NaN	NaN	NaN	NaN
108762	108762	NZL	Oceania	New Zealand	2021-12-23	13719.0	71.0	57.429	49.0	0.0	...	14.8	17.2	NaN	2.61	82.29	0.931	NaN	NaN	NaN	NaN
55907	55907	GAB	Africa	Gabon	2021-12-23	38039.0	0.0	51.143	286.0	0.0	...	NaN	NaN	NaN	6.30	66.47	0.703	NaN	NaN	NaN	NaN
71009	71009	IDN	Asia	Indonesia	2021-12-23	4261208.0	136.0	193.000	144042.0	8.0	...	2.8	76.1	64.204	1.04	71.72	0.718	NaN	NaN	NaN	NaN

5 rows × 68 columns

fig = px.scatter_geo(covid_day_df, locations="iso_code", color="continent",
                     hover_name="location", size="new_cases_smoothed",
                     projection="natural earth", template="plotly_dark")

fig.show()

Covid-19 Total Cases over the days

fig = px.scatter_geo(covid_df, locations="iso_code", color="continent",
                     hover_name="location", size="total_cases",
                     projection="natural earth", animation_frame="date", template="plotly_dark")

fig.show()

MNIST Dataset

import torchvision
import os

import matplotlib.pyplot as plt
from matplotlib import rc
from matplotlib.animation import FuncAnimation
from matplotlib import animation

rc('animation', html='jshtml')

frn = 10 # Number of frames to process in the animation
fps = 0.5 # Frames per second
mywriter = animation.PillowWriter(fps=fps) 

mnist_dataset = torchvision.datasets.MNIST(root = "data/mnist", train = True, download = True, transform=torchvision.transforms.ToTensor())

fig, ax = plt.subplots(figsize = (10, 10))

def change_plot(frame_idx):
    ax.cla()
    image_tensor = mnist_dataset[frame_idx][0]
    image_tensor_gray = image_tensor[0]
    image_tensor_gray = image_tensor_gray * 255
    ax.matshow(image_tensor_gray, cmap = "gray")
    for i in range(image_tensor_gray.shape[0]):
        for j in range(image_tensor_gray.shape[1]):
            ax.text(i, j, str(int(image_tensor_gray[j][i].item())), va = "center", ha = "center", color = "blue", fontsize = "small")
    ax.axis("off")
    plt.tight_layout()

anim = FuncAnimation(fig, change_plot, frn, interval=1000 / fps)
plt.close()
anim

Once Loop Reflect

<Figure size 432x288 with 0 Axes>

mywriter = animation.PillowWriter(fps=fps)

if not os.path.exists("./assets/gif"):
    os.makedir("./assets/gif")
    
anim.save('./assets/gif/mnist.gif',writer=mywriter)

<Figure size 432x288 with 0 Axes>